XGBoost is one of the most powerful tools for structured/tabular data — known for its speed, scalability, and high performance. In this post, I’ve shared a detailed explanation of what makes XGBoost so effective, along with its history, features, and real-world use. A great resource for anyone learning ML!

Eigenvectors and eigenvalues reveal how matrices reshape space. From understanding linear transformations to exploring rotation axes and dimensionality reduction in PCA, this post dives into the heart of matrix magic—explained visually, intuitively, and practically.

Understand K-Nearest Neighbors (KNN), a lazy learning algorithm that predicts by finding the closest training data points. Explore how it works, its classification and regression modes, key hyperparameters, overfitting/underfitting issues, and optimized search structures like KD-Tree and Ball Tree for efficient computation.